Understanding the European energy crisis through structural causal models

Area of Science:

• Economics
• Energy Policy
• Causal Inference

Background:

• European energy markets faced unprecedented volatility due to Russia's invasion of Ukraine.
• France experienced significant electricity price increases despite a low reliance on natural gas.
• Traditional correlation studies yielded paradoxical results in understanding these market dynamics.

Purpose of the Study:

• To apply causal statistical methods to investigate the French and Spanish electricity markets.
• To identify key factors influencing electricity prices and net exports.
• To demonstrate the advantages of causal inference over simple correlation for complex energy market analysis.

Main Methods:

• Development and application of causal graphs to model market interdependencies.
• Implementation of a linear structural causal model.
• Utilizing non-linear tree-based machine learning combined with Shapley Flow values for quantitative analysis.

Main Results:

• Causal analysis resolved paradoxical findings from correlation studies.
• Identified the interplay between natural gas prices and nuclear power plant unavailability as critical factors.
• Quantified indirect causal effects and enabled "what-if" scenario analysis.

Conclusions:

• Causal inference is crucial for accurately understanding energy market dynamics.
• High nuclear power plant unavailability significantly increased France's import dependency during the energy crisis.
• The study provides a robust framework for analyzing energy market shocks and informing policy.